swmm.pandas.Output.node_attribute¶

Output.node_attribute(time, attribute=('invert_depth', 'flooding_losses', 'total_inflow'), asframe=True)[source]¶

For all nodes at a given time, get one or more attributes.

Parameters

time: Union[str, int, datetime]

The datetime or simulation index for which to pull data, defaults to None

attribute: Union[int, str, Enum, Sequence[Union[int, str, Enum]], None],

The attribute index or name.

On of:

invert_depth, hydraulic_head, ponded_volume, lateral_inflow, total_inflow, flooding_losses.

defaults to: (‘invert_depth’,’flooding_losses’,’total_inflow’)

Can also input the integer index of the attribute you would like to pull or the actual enum from Output.node_attributes.

Setting to None indicates all attributes.

asframe: bool

A switch to return an indexed DataFrame. Set to False to get an array of values only, defaults to True.

Returns

Union[pd.DataFrame, np.ndarray]

A DataFrame or ndarray of attribute values in each column for requested simulation time.

Examples

Pull all attributes from middle of simulation

>>> from swmm.pandas import Output,test_out_path
>>> out = Output(test_out_path)
>>> out.node_attribute(out.period/2)
           invert_depth  hydraulic_head  ponded_volume  ...  groundwater  pol_rainfall    sewage
    node                                                ...
    JUNC1      8.677408       10.177408       0.000000  ...     0.260937     99.739067  0.000000
    JUNC2      4.286304        3.246305       0.000000  ...     0.366218     96.767433  2.475719
    JUNC3     11.506939        8.036940      35.862713  ...     0.615687     94.522049  4.862284
    JUNC4     14.936149        9.686150    6107.279785  ...     0.381425     96.532028  3.086555
    JUNC5     11.190232        4.690233       0.000000  ...     0.443388     95.959351  3.597255
    JUNC6      1.650765        1.650765       0.000000  ...     0.963940     91.113075  7.922997
    OUT1       0.946313        1.046313       0.000000  ...     0.969624     91.060143  7.970241
    OUT2       0.000000       -1.040001       0.000000  ...     0.367271     96.756134  2.479369
    STOR1     18.282972        3.032968    7550.865723  ...     0.961457     91.136200  7.902364
    [9 rows x 9 columns]